基于TCN-Transformer的工业机器人自动异常检测

doi:10.3901/JME.260405

摘要/Abstract

摘要： 针对工业机器人出厂检测场景自动化异常检测的需求，提出一种基于TCN-Transformer的工业机器人自动异常检测方法。设计基于TCN-Transformer的工业机器人动力学建模方法，将工业机器人各关节实时角度、角速度和角加速度输入TCN-Transformer模型进行逆动力学求解生成自适应实时工况的各关节标准电流信号，将其与实时电流信号进行相似度量实现自适应异常检测。构建云边协同的工业机器人自动异常检测系统，对生产测试区的工业机器人自动获取信息、采集实时数据和自动异常检测。并进行工业机器人多工况实验、关节异常注入实验和系统压力测试，验证了该动力学建模方法自适应生成标准数据有较高的准确性，该异常检测方法能够定位异常关节并与异常程度有较高的一致性，该系统能够为大批量工业机器人集群提供稳定准确高效的自动异常检测。

关键词: 工业机器人, 动力学建模, Transformer模型, 异常检测, 云边协同

Abstract: To address the need for automated anomaly detection in industrial robot factory inspections, a novel industrial robot automatic anomaly detection method based on the TCN-Transformer model is proposed. The method designs a TCN-Transformer-based dynamic modeling approach for industrial robots, where real-time joint angles, angular velocities, and angular accelerations are input into the TCN-Transformer model to perform inverse dynamics. This generates adaptive real-time standard joint current signals, which are compared with the actual real-time current signals to measure similarity and enable adaptive anomaly detection. A cloud-edge collaborative industrial robot automatic anomaly detection system is built, enabling automatic information acquisition, real-time data collection, and automated anomaly detection for robots in the production testing area. Through multiple experimental scenarios, including multi-condition robot tests, joint anomaly injection experiments, and system stress testing, the proposed dynamic modeling method is shown to generate adaptive standard data with high accuracy. The anomaly detection method is able to locate faulty joints and maintain a high consistency with the severity of the anomaly. This system provides stable, accurate, and efficient automated anomaly detection for large-scale industrial robot clusters.

Key words: industrial robots, dynamics modeling, Transformer model, anomaly detection, cloud-edge collaboration

中图分类号:

TP242

蒋沁诚, 陶建峰, 汪世杰, 王洋洋, 刘成良. 基于TCN-Transformer的工业机器人自动异常检测[J]. 机械工程学报, 2026, 62(9): 1-13.

JIANG Qincheng, TAO Jianfeng, WANG Shijie, WANG Yangyang, LIU Chengliang. TCN-Transformer-based Automatic Anomaly Detection for Industrial Robots[J]. Journal of Mechanical Engineering, 2026, 62(9): 1-13.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260405

http://www.cjmenet.com.cn/CN/Y2026/V62/I9/1

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